Techniques for image recognition and comparison using optical associative memory configurations have been proposed, such as the one known as the Hopfield type described in the publication by J. J. Hopfield, "Neural networks and Physical systems with emergent collective computational abilities", Proc. Natl. Acad. Sci. U.S.A. 79, 2554 (1984).
It is known that the Hopfield associative memory model is more effective for orthogonal memory images. However, real-world images, such as cars or airplanes, usually contain lots of common parts so that the associative recall of a complete image using a corrupted partial image input is relatively difficult.
What is needed is an implementation of a Hopfield type associative memory that can improve the success rate of associative optical recall even with partial and/or corrupted input images.